Overview

Dataset statistics

Number of variables11
Number of observations953
Missing cells95
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory228.9 KiB
Average record size in memory245.9 B

Variable types

Text1
Numeric8
Categorical2

Alerts

key has 95 (10.0%) missing valuesMissing
track_id has unique valuesUnique
acousticness_% has 60 (6.3%) zerosZeros
instrumentalness_% has 866 (90.9%) zerosZeros

Reproduction

Analysis started2024-06-26 16:55:19.293831
Analysis finished2024-06-26 16:55:25.606356
Duration6.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

track_id
Text

UNIQUE 

Distinct953
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
2024-06-26T10:55:25.815923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.996852
Min length4

Characters and Unicode

Total characters6668
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique953 ?
Unique (%)100.0%

Sample

1st row4082370
2nd row6247887
3rd row6974739
4th row2362023
5th row4386478
ValueCountFrequency (%)
4082370 1
 
0.1%
5374189 1
 
0.1%
3197174 1
 
0.1%
6974739 1
 
0.1%
2362023 1
 
0.1%
4386478 1
 
0.1%
5031502 1
 
0.1%
1892693 1
 
0.1%
7366228 1
 
0.1%
5563881 1
 
0.1%
Other values (943) 943
99.0%
2024-06-26T10:55:26.146977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 752
11.3%
6 733
11.0%
2 716
10.7%
4 689
10.3%
1 689
10.3%
7 681
10.2%
5 673
10.1%
8 642
9.6%
0 551
8.3%
9 541
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6667
> 99.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 752
11.3%
6 733
11.0%
2 716
10.7%
4 689
10.3%
1 689
10.3%
7 681
10.2%
5 673
10.1%
8 642
9.6%
0 551
8.3%
9 541
8.1%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6668
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 752
11.3%
6 733
11.0%
2 716
10.7%
4 689
10.3%
1 689
10.3%
7 681
10.2%
5 673
10.1%
8 642
9.6%
0 551
8.3%
9 541
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 752
11.3%
6 733
11.0%
2 716
10.7%
4 689
10.3%
1 689
10.3%
7 681
10.2%
5 673
10.1%
8 642
9.6%
0 551
8.3%
9 541
8.1%

bpm
Real number (ℝ)

Distinct124
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.5404
Minimum65
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:26.311488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile81
Q1100
median121
Q3140
95-th percentile174
Maximum206
Range141
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.057802
Coefficient of variation (CV)0.22896777
Kurtosis-0.39902742
Mean122.5404
Median Absolute Deviation (MAD)21
Skewness0.41324555
Sum116781
Variance787.24023
MonotonicityNot monotonic
2024-06-26T10:55:26.400423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 39
 
4.1%
140 31
 
3.3%
130 31
 
3.3%
92 25
 
2.6%
110 24
 
2.5%
150 21
 
2.2%
90 21
 
2.2%
122 19
 
2.0%
105 19
 
2.0%
125 18
 
1.9%
Other values (114) 705
74.0%
ValueCountFrequency (%)
65 2
 
0.2%
67 1
 
0.1%
71 3
 
0.3%
72 3
 
0.3%
73 1
 
0.1%
74 1
 
0.1%
75 1
 
0.1%
76 2
 
0.2%
77 4
0.4%
78 9
0.9%
ValueCountFrequency (%)
206 2
0.2%
204 1
0.1%
202 2
0.2%
200 1
0.1%
198 1
0.1%
196 1
0.1%
192 1
0.1%
189 1
0.1%
188 1
0.1%
186 2
0.2%

key
Categorical

MISSING 

Distinct11
Distinct (%)1.3%
Missing95
Missing (%)10.0%
Memory size52.8 KiB
C#
120 
G
96 
G#
91 
F
89 
B
81 
Other values (6)
381 

Length

Max length2
Median length1
Mean length1.4358974
Min length1

Characters and Unicode

Total characters1232
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC#
3rd rowF
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
C# 120
12.6%
G 96
10.1%
G# 91
9.5%
F 89
9.3%
B 81
8.5%
D 81
8.5%
A 75
7.9%
F# 73
7.7%
E 62
6.5%
A# 57
6.0%
(Missing) 95
10.0%

Length

2024-06-26T10:55:26.483124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g 187
21.8%
f 162
18.9%
a 132
15.4%
c 120
14.0%
d 114
13.3%
b 81
9.4%
e 62
 
7.2%

Most occurring characters

ValueCountFrequency (%)
# 374
30.4%
G 187
15.2%
F 162
13.1%
A 132
 
10.7%
C 120
 
9.7%
D 114
 
9.3%
B 81
 
6.6%
E 62
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 858
69.6%
Other Punctuation 374
30.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 187
21.8%
F 162
18.9%
A 132
15.4%
C 120
14.0%
D 114
13.3%
B 81
9.4%
E 62
 
7.2%
Other Punctuation
ValueCountFrequency (%)
# 374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 858
69.6%
Common 374
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 187
21.8%
F 162
18.9%
A 132
15.4%
C 120
14.0%
D 114
13.3%
B 81
9.4%
E 62
 
7.2%
Common
ValueCountFrequency (%)
# 374
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 374
30.4%
G 187
15.2%
F 162
13.1%
A 132
 
10.7%
C 120
 
9.7%
D 114
 
9.3%
B 81
 
6.6%
E 62
 
5.0%

mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
Major
550 
Minor
403 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4765
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor
2nd rowMajor
3rd rowMajor
4th rowMajor
5th rowMinor

Common Values

ValueCountFrequency (%)
Major 550
57.7%
Minor 403
42.3%

Length

2024-06-26T10:55:26.553330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-26T10:55:26.630478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
major 550
57.7%
minor 403
42.3%

Most occurring characters

ValueCountFrequency (%)
M 953
20.0%
o 953
20.0%
r 953
20.0%
a 550
11.5%
j 550
11.5%
i 403
8.5%
n 403
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3812
80.0%
Uppercase Letter 953
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 953
25.0%
r 953
25.0%
a 550
14.4%
j 550
14.4%
i 403
10.6%
n 403
10.6%
Uppercase Letter
ValueCountFrequency (%)
M 953
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4765
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 953
20.0%
o 953
20.0%
r 953
20.0%
a 550
11.5%
j 550
11.5%
i 403
8.5%
n 403
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 953
20.0%
o 953
20.0%
r 953
20.0%
a 550
11.5%
j 550
11.5%
i 403
8.5%
n 403
8.5%

danceability_%
Real number (ℝ)

Distinct72
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.96957
Minimum23
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:26.702580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile40.6
Q157
median69
Q378
95-th percentile89
Maximum96
Range73
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.63061
Coefficient of variation (CV)0.21846654
Kurtosis-0.33356575
Mean66.96957
Median Absolute Deviation (MAD)10
Skewness-0.43587813
Sum63822
Variance214.05475
MonotonicityNot monotonic
2024-06-26T10:55:26.886165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 43
 
4.5%
77 32
 
3.4%
80 31
 
3.3%
56 30
 
3.1%
74 29
 
3.0%
81 28
 
2.9%
73 27
 
2.8%
78 26
 
2.7%
71 26
 
2.7%
65 26
 
2.7%
Other values (62) 655
68.7%
ValueCountFrequency (%)
23 1
 
0.1%
24 1
 
0.1%
25 1
 
0.1%
27 1
 
0.1%
28 2
 
0.2%
29 1
 
0.1%
31 4
0.4%
32 2
 
0.2%
33 3
0.3%
34 7
0.7%
ValueCountFrequency (%)
96 1
 
0.1%
95 6
0.6%
94 1
 
0.1%
93 5
0.5%
92 10
1.0%
91 12
1.3%
90 9
0.9%
89 7
0.7%
88 7
0.7%
87 10
1.0%

valence_%
Real number (ℝ)

Distinct94
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.43127
Minimum4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:26.975431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q132
median51
Q370
95-th percentile90
Maximum97
Range93
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.480632
Coefficient of variation (CV)0.45654389
Kurtosis-0.93933627
Mean51.43127
Median Absolute Deviation (MAD)19
Skewness0.0082235369
Sum49014
Variance551.34007
MonotonicityNot monotonic
2024-06-26T10:55:27.058165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 21
 
2.2%
40 20
 
2.1%
59 18
 
1.9%
53 18
 
1.9%
55 18
 
1.9%
61 17
 
1.8%
49 16
 
1.7%
22 16
 
1.7%
50 15
 
1.6%
42 15
 
1.6%
Other values (84) 779
81.7%
ValueCountFrequency (%)
4 5
0.5%
5 2
 
0.2%
6 3
0.3%
7 5
0.5%
8 4
0.4%
9 2
 
0.2%
10 6
0.6%
11 6
0.6%
12 6
0.6%
13 5
0.5%
ValueCountFrequency (%)
97 5
 
0.5%
96 13
1.4%
95 1
 
0.1%
94 4
 
0.4%
93 4
 
0.4%
92 9
0.9%
91 7
0.7%
90 10
1.0%
89 6
0.6%
88 9
0.9%

energy_%
Real number (ℝ)

Distinct80
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.279119
Minimum9
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:27.144613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile34.6
Q153
median66
Q377
95-th percentile89
Maximum97
Range88
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.550526
Coefficient of variation (CV)0.25747904
Kurtosis-0.25998229
Mean64.279119
Median Absolute Deviation (MAD)12
Skewness-0.44639922
Sum61258
Variance273.91991
MonotonicityNot monotonic
2024-06-26T10:55:27.222081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 29
 
3.0%
62 28
 
2.9%
76 27
 
2.8%
66 25
 
2.6%
73 23
 
2.4%
60 23
 
2.4%
68 23
 
2.4%
79 22
 
2.3%
80 22
 
2.3%
70 22
 
2.3%
Other values (70) 709
74.4%
ValueCountFrequency (%)
9 1
 
0.1%
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
20 4
0.4%
23 1
 
0.1%
24 4
0.4%
25 3
0.3%
26 2
0.2%
27 3
0.3%
ValueCountFrequency (%)
97 2
 
0.2%
96 2
 
0.2%
95 1
 
0.1%
94 6
0.6%
93 3
 
0.3%
92 4
 
0.4%
91 9
0.9%
90 12
1.3%
89 14
1.5%
88 12
1.3%

acousticness_%
Real number (ℝ)

ZEROS 

Distinct98
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.057712
Minimum0
Maximum97
Zeros60
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:27.301315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q343
95-th percentile81.4
Maximum97
Range97
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.996077
Coefficient of variation (CV)0.96076405
Kurtosis-0.19208351
Mean27.057712
Median Absolute Deviation (MAD)15
Skewness0.9524617
Sum25786
Variance675.79604
MonotonicityNot monotonic
2024-06-26T10:55:27.383112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
6.3%
1 48
 
5.0%
4 35
 
3.7%
2 33
 
3.5%
5 30
 
3.1%
3 30
 
3.1%
9 29
 
3.0%
6 29
 
3.0%
11 24
 
2.5%
7 22
 
2.3%
Other values (88) 613
64.3%
ValueCountFrequency (%)
0 60
6.3%
1 48
5.0%
2 33
3.5%
3 30
3.1%
4 35
3.7%
5 30
3.1%
6 29
3.0%
7 22
 
2.3%
8 17
 
1.8%
9 29
3.0%
ValueCountFrequency (%)
97 2
 
0.2%
96 1
 
0.1%
95 1
 
0.1%
94 2
 
0.2%
93 2
 
0.2%
92 3
0.3%
91 5
0.5%
90 3
0.3%
89 2
 
0.2%
88 2
 
0.2%

instrumentalness_%
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5813221
Minimum0
Maximum91
Zeros866
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:27.457169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum91
Range91
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.4097999
Coefficient of variation (CV)5.3182079
Kurtosis56.635596
Mean1.5813221
Median Absolute Deviation (MAD)0
Skewness7.1242172
Sum1507
Variance70.724735
MonotonicityNot monotonic
2024-06-26T10:55:27.529587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 866
90.9%
1 21
 
2.2%
2 7
 
0.7%
4 5
 
0.5%
3 4
 
0.4%
5 4
 
0.4%
9 3
 
0.3%
63 3
 
0.3%
18 3
 
0.3%
6 3
 
0.3%
Other values (29) 34
 
3.6%
ValueCountFrequency (%)
0 866
90.9%
1 21
 
2.2%
2 7
 
0.7%
3 4
 
0.4%
4 5
 
0.5%
5 4
 
0.4%
6 3
 
0.3%
8 2
 
0.2%
9 3
 
0.3%
10 2
 
0.2%
ValueCountFrequency (%)
91 1
 
0.1%
90 1
 
0.1%
83 1
 
0.1%
72 1
 
0.1%
63 3
0.3%
61 1
 
0.1%
51 2
0.2%
47 1
 
0.1%
46 1
 
0.1%
44 1
 
0.1%

liveness_%
Real number (ℝ)

Distinct68
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.213012
Minimum3
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:27.608383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median12
Q324
95-th percentile44.4
Maximum97
Range94
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.711223
Coefficient of variation (CV)0.75282571
Kurtosis5.7143954
Mean18.213012
Median Absolute Deviation (MAD)4
Skewness2.10428
Sum17357
Variance187.99765
MonotonicityNot monotonic
2024-06-26T10:55:27.692560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 102
 
10.7%
9 93
 
9.8%
10 78
 
8.2%
12 72
 
7.6%
8 54
 
5.7%
13 47
 
4.9%
7 38
 
4.0%
15 36
 
3.8%
14 29
 
3.0%
6 26
 
2.7%
Other values (58) 378
39.7%
ValueCountFrequency (%)
3 4
 
0.4%
4 5
 
0.5%
5 16
 
1.7%
6 26
 
2.7%
7 38
 
4.0%
8 54
5.7%
9 93
9.8%
10 78
8.2%
11 102
10.7%
12 72
7.6%
ValueCountFrequency (%)
97 1
0.1%
92 1
0.1%
91 1
0.1%
90 1
0.1%
83 1
0.1%
80 2
0.2%
77 1
0.1%
72 2
0.2%
67 1
0.1%
66 2
0.2%

speechiness_%
Real number (ℝ)

Distinct48
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.131165
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-06-26T10:55:27.777650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q311
95-th percentile33
Maximum64
Range62
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.9128876
Coefficient of variation (CV)0.97845488
Kurtosis3.3744263
Mean10.131165
Median Absolute Deviation (MAD)2
Skewness1.9346683
Sum9655
Variance98.265341
MonotonicityNot monotonic
2024-06-26T10:55:27.858997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
4 175
18.4%
3 152
15.9%
5 130
13.6%
6 76
 
8.0%
8 52
 
5.5%
7 49
 
5.1%
9 37
 
3.9%
10 24
 
2.5%
11 22
 
2.3%
12 16
 
1.7%
Other values (38) 220
23.1%
ValueCountFrequency (%)
2 3
 
0.3%
3 152
15.9%
4 175
18.4%
5 130
13.6%
6 76
8.0%
7 49
 
5.1%
8 52
 
5.5%
9 37
 
3.9%
10 24
 
2.5%
11 22
 
2.3%
ValueCountFrequency (%)
64 1
 
0.1%
59 1
 
0.1%
49 1
 
0.1%
46 3
0.3%
45 2
0.2%
44 2
0.2%
43 1
 
0.1%
42 1
 
0.1%
41 1
 
0.1%
40 4
0.4%

Interactions

2024-06-26T10:55:24.744341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:19.531797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.182559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.929731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.866180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.533579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.362404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.984995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.832231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:19.614726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.277395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.038438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.949167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.622484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.440310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.063775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.911374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:19.701965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.371284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.121692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.032146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.720772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.521601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.285674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.986018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:19.785618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.464974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.196274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.107162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.807914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.600879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.361293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:25.063982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:19.859107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.554277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.271930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.209181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.040454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.684566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.446417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:25.170367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:19.934271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.641075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.356212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.294315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.117609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.761106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.521909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:25.246452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.011504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.728423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.672040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.369003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.195293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.831200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.593854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:25.322484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.100211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:20.833884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:21.779033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:22.448031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.282065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:23.909298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-06-26T10:55:24.666624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2024-06-26T10:55:27.933464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
bpmdanceability_%valence_%energy_%acousticness_%instrumentalness_%liveness_%speechiness_%keymode
bpm1.000-0.1240.0420.028-0.034-0.0400.0000.0440.0310.000
danceability_%-0.1241.0000.4000.148-0.147-0.074-0.0930.2830.0640.118
valence_%0.0420.4001.0000.353-0.019-0.139-0.0150.1110.0730.000
energy_%0.0280.1480.3531.000-0.475-0.0540.0460.0900.0150.091
acousticness_%-0.034-0.147-0.019-0.4751.0000.049-0.028-0.0480.0380.077
instrumentalness_%-0.040-0.074-0.139-0.0540.0491.000-0.033-0.1250.0260.000
liveness_%0.000-0.093-0.0150.046-0.028-0.0331.000-0.0210.0000.000
speechiness_%0.0440.2830.1110.090-0.048-0.125-0.0211.0000.0250.031
key0.0310.0640.0730.0150.0380.0260.0000.0251.0000.277
mode0.0000.1180.0000.0910.0770.0000.0000.0310.2771.000

Missing values

2024-06-26T10:55:25.430580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-26T10:55:25.539789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

track_idbpmkeymodedanceability_%valence_%energy_%acousticness_%instrumentalness_%liveness_%speechiness_%
04082370125BMajor80898331084
1624788792C#Major71617470104
26974739138FMajor513253170316
32362023170AMajor5558721101115
44386478144AMinor6523801463116
55031502141C#Major926658190824
61892693148FMinor67837648083
77366228100FMajor672671370114
85563881130C#Minor852262120289
95586506170DMinor815648210833
track_idbpmkeymodedanceability_%valence_%energy_%acousticness_%instrumentalness_%liveness_%speechiness_%
9434322356144FMajor936261001220
9444343383125FMajor54227600143
9453108815142FMinor854043403932
9468174233120DMajor641153102527
947158745296FMajor57557422084
9483476877144AMajor60243957083
9492341529166F#Major42724831126
950855373492C#Major8081674086
951677304397C#Major82677780125
952672803790EMinor613267150115